Synthetic AI Agents for Experimental Social Science

8 pages•Published: April 19, 2026

Abstract

Large language models (LLMs) are transforming the social science toolkit. Synthetic AI agents are LLM-powered programs that can reason, converse, and act autonomously. This position paper argues that these developments offer three AI-human complementary research uses. First, as interactive partners, AI agents let scholars probe human-AI social dynamics to learn more about human dynamics. Second, finely-tuned AI models can be synthetic substitutes for human subjects. This will allow rapid piloting of novel experimental designs, cross-cultural “synthetic cultural agents”, and generating “out-of-this-world" speculations about experiments that can't be conducted with people. Third, as analytic tools, AI agents can code, summarize, and even simulate qualitative data at scales unreachable by humans alone. We synthesize recent evidence, highlight ethical and methodological pitfalls, including bias, prompt sensitivity, and the limits of fully automated analysis, and outline elements of a research agenda in which AI complements human subjects and scientists.

Keyphrases: behavioral science, human ai complementarity, synthetic ai subjects

In: Jernej Masnec, Hamid Reza Karimian, Parisa Kordjamshidi and Yan Li (editors). Proceedings of AI for Accelerated Research Symposium, vol 3, pages 77-84.

Links:	https://easychair.org/publications/paper/blxq
	https://doi.org/10.29007/xj88